Bird.Id was created as an early project for Fast.ai’s deep learning course. The primary aim was to complete an end-to-end example.
The dataset consists of images of 10 bird species, the ones mostly commonly found in New Zealand’s forests, and totals ~1000 raw images. Data transformations from the fastai library are used to increase the size of the dataset for model training.
Having read, listened-to, and tried a number of approaches to deep learning, the approach outlined above is, I believe, the easiest learning pathway to a functioning neural net using current tooling.
Bird.Id is split between a user interface (UI) and a REST API which hosts the model. Both are hosted on Azure using low tiers.
- Build the UI in the language/framework you know as it’s the least interesting part of the project. I used ASP.NET Core.
- The REST API is built with Docker, Gunicorn, Flask, and Python. Useful links are:
Go to Bird.Id